Prediction of temperature factors from protein sequence.
Prediction of temperature factors from protein sequence.
复制标题
DOI：
10.6026/97320630009134
复制
发表时间：
2013
期刊：
影响因子：
1.9
通讯作者：
Jadhav AG
中科院分区：
文献类型：
--
作者：
Sonavane S;Jaybhaye AA;Jadhav AG
蛋白质柔性在蛋白质的结构和功能方面是有用的。我们已经分析了局部一级蛋白质序列特征，其组合可以直接从蛋白质序列预测氨基酸残基的B值。我们还分析了B值在蛋白质三维结构不同区域的分布。平均而言，残基与蛋白质表面的距离每增加0.5 μ m，归一化B值降低0.1055。与存在于其他常规二级结构元件中的残基相比，环区中的残基具有更高的B值。存在于蛋白质核心中的掩埋残基比存在于蛋白质表面上的残基更刚性（较低的B值）。类似地，倾向于存在于蛋白质核心中的疏水残基具有比极性残基更低的平均B值。最后，提出了基于支持向量回归（SVR）的蛋白质一级序列B值预测方法。我们的结果表明，SVR模型达到了0.47的相关系数，这是与现有的方法相媲美。
Protein flexibility is useful in structural and functional aspect of proteins. We have analyzed the local primary protein sequence features that in combination can predict the B-value of amino acid residues directly from the protein sequence. We have also analyzed the distribution of B-value in different regions of protein three dimensional structures. On an average, the normalized Bvalue decreases by 0.1055 with every 0.5Å increase in the distance of the residue from protein surface. The residues in the loop regions have higher B-values as compared to the residues present in other regular secondary structural elements. Buried residues which are present in the protein core are more rigid (lower B-values) than the residues present on the protein surface. Similarly, the hydrophobic residues which tend to be present in the protein core have lower average B-value than the polar residues. Finally, we have proposed the method based on Support Vector Regression (SVR) to predict the B-value from protein primary sequence. Our result shows that, the SVR model achieved the correlation coefficient of 0.47 which is comparable to existing methods.